IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i4d10.1007_s10845-017-1350-2.html
   My bibliography  Save this article

Review of job shop scheduling research and its new perspectives under Industry 4.0

Author

Listed:
  • Jian Zhang

    (Southwest Jiaotong University)

  • Guofu Ding

    (Southwest Jiaotong University)

  • Yisheng Zou

    (Southwest Jiaotong University)

  • Shengfeng Qin

    (Northumbria University)

  • Jianlin Fu

    (Southwest Jiaotong University)

Abstract

Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems, Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

Suggested Citation

  • Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1350-2
    DOI: 10.1007/s10845-017-1350-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-017-1350-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-017-1350-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jeffrey R. Barker & Graham B. McMahon, 1985. "Scheduling the General Job-Shop," Management Science, INFORMS, vol. 31(5), pages 594-598, May.
    2. S Q Liu & E Kozan, 2012. "A hybrid shifting bottleneck procedure algorithm for the parallel-machine job-shop scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(2), pages 168-182, February.
    3. Chris N. Potts & Luk N. Van Wassenhove, 1985. "A Branch and Bound Algorithm for the Total Weighted Tardiness Problem," Operations Research, INFORMS, vol. 33(2), pages 363-377, April.
    4. Midhun Paul & R. Sridharan & T. Radha Ramanan, 2016. "A multi-objective decision-making framework using preference selection index for assembly job shop scheduling problem," International Journal of Management Concepts and Philosophy, Inderscience Enterprises Ltd, vol. 9(4), pages 362-387.
    5. Harvey M. Wagner, 1959. "An integer linear‐programming model for machine scheduling," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 6(2), pages 131-140, June.
    6. Stephen C. Graves, 1981. "A Review of Production Scheduling," Operations Research, INFORMS, vol. 29(4), pages 646-675, August.
    7. Sakawa, Masatoshi & Kubota, Ryo, 2000. "Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms," European Journal of Operational Research, Elsevier, vol. 120(2), pages 393-407, January.
    8. B. Bullnheimer & R.F. Hartl & C. Strauss, 1999. "An improved Ant System algorithm for theVehicle Routing Problem," Annals of Operations Research, Springer, vol. 89(0), pages 319-328, January.
    9. Huang, Rong-Hwa & Yang, Chang-Lin & Cheng, Wei-Che, 2013. "Flexible job shop scheduling with due window—a two-pheromone ant colony approach," International Journal of Production Economics, Elsevier, vol. 141(2), pages 685-697.
    10. Erwin Pesch & Ulrich A. W. Tetzlaff, 1996. "Constraint Propagation Based Scheduling of Job Shops," INFORMS Journal on Computing, INFORMS, vol. 8(2), pages 144-157, May.
    11. Egon Balas & Alkis Vazacopoulos, 1998. "Guided Local Search with Shifting Bottleneck for Job Shop Scheduling," Management Science, INFORMS, vol. 44(2), pages 262-275, February.
    12. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    13. N. Hefetz & I. Adiri, 1982. "An Efficient Optimal Algorithm for the Two-Machines Unit-Time Jobshop Schedule-Length Problem," Mathematics of Operations Research, INFORMS, vol. 7(3), pages 354-360, August.
    14. Graham McMahon & Michael Florian, 1975. "On Scheduling with Ready Times and Due Dates to Minimize Maximum Lateness," Operations Research, INFORMS, vol. 23(3), pages 475-482, June.
    15. Christodoulos Floudas & Xiaoxia Lin, 2005. "Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications," Annals of Operations Research, Springer, vol. 139(1), pages 131-162, October.
    16. Alan S. Manne, 1960. "On the Job-Shop Scheduling Problem," Operations Research, INFORMS, vol. 8(2), pages 219-223, April.
    17. S. M. Johnson, 1954. "Optimal two‐ and three‐stage production schedules with setup times included," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(1), pages 61-68, March.
    18. M. R. Garey & D. S. Johnson & Ravi Sethi, 1976. "The Complexity of Flowshop and Jobshop Scheduling," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 117-129, May.
    19. Joseph Adams & Egon Balas & Daniel Zawack, 1988. "The Shifting Bottleneck Procedure for Job Shop Scheduling," Management Science, INFORMS, vol. 34(3), pages 391-401, March.
    20. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    21. Fuqing Zhao & Zhongshi Shao & Junbiao Wang & Chuck Zhang, 2016. "A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1039-1060, February.
    22. Jones, D. F. & Mirrazavi, S. K. & Tamiz, M., 2002. "Multi-objective meta-heuristics: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 137(1), pages 1-9, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raja Awais Liaqait & Shermeen Hamid & Salman Sagheer Warsi & Azfar Khalid, 2021. "A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    2. Blazewicz, Jacek & Domschke, Wolfgang & Pesch, Erwin, 1996. "The job shop scheduling problem: Conventional and new solution techniques," European Journal of Operational Research, Elsevier, vol. 93(1), pages 1-33, August.
    3. Jain, A. S. & Meeran, S., 1999. "Deterministic job-shop scheduling: Past, present and future," European Journal of Operational Research, Elsevier, vol. 113(2), pages 390-434, March.
    4. Da Col, Giacomo & Teppan, Erich C., 2022. "Industrial-size job shop scheduling with constraint programming," Operations Research Perspectives, Elsevier, vol. 9(C).
    5. Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.
    6. C N Potts & V A Strusevich, 2009. "Fifty years of scheduling: a survey of milestones," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 41-68, May.
    7. Guinet, Alain & Legrand, Marie, 1998. "Reduction of job-shop problems to flow-shop problems with precedence constraints," European Journal of Operational Research, Elsevier, vol. 109(1), pages 96-110, August.
    8. Golenko-Ginzburg, Dimitri & Kesler, Shmuel & Landsman, Zinoviy, 1995. "Industrial job-shop scheduling with random operations and different priorities," International Journal of Production Economics, Elsevier, vol. 40(2-3), pages 185-195, August.
    9. Bertsimas, Dimitris & Gupta, Shubham & Lulli, Guglielmo, 2014. "Dynamic resource allocation: A flexible and tractable modeling framework," European Journal of Operational Research, Elsevier, vol. 236(1), pages 14-26.
    10. Levorato, Mario & Figueiredo, Rosa & Frota, Yuri, 2022. "Exact solutions for the two-machine robust flow shop with budgeted uncertainty," European Journal of Operational Research, Elsevier, vol. 300(1), pages 46-57.
    11. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.
    12. Gerardo Minella & Rubén Ruiz & Michele Ciavotta, 2008. "A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 20(3), pages 451-471, August.
    13. Ramalhinho Lourenco, Helena, 1996. "Sevast'yanov's algorithm for the flow-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 91(1), pages 176-189, May.
    14. Edzard Weber & Anselm Tiefenbacher & Norbert Gronau, 2019. "Need for Standardization and Systematization of Test Data for Job-Shop Scheduling," Data, MDPI, vol. 4(1), pages 1-21, February.
    15. Li, Wei & Nault, Barrie R. & Ye, Honghan, 2019. "Trade-off balancing in scheduling for flow shop production and perioperative processes," European Journal of Operational Research, Elsevier, vol. 273(3), pages 817-830.
    16. Xinyu Yao & Karmel S. Shehadeh & Rema Padman, 2024. "Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach," Health Care Management Science, Springer, vol. 27(3), pages 352-369, September.
    17. Zhang, Rui & Chang, Pei-Chann & Wu, Cheng, 2013. "A hybrid genetic algorithm for the job shop scheduling problem with practical considerations for manufacturing costs: Investigations motivated by vehicle production," International Journal of Production Economics, Elsevier, vol. 145(1), pages 38-52.
    18. Kan Fang & Nelson Uhan & Fu Zhao & John Sutherland, 2013. "Flow shop scheduling with peak power consumption constraints," Annals of Operations Research, Springer, vol. 206(1), pages 115-145, July.
    19. Madiha Harrabi & Olfa Belkahla Driss & Khaled Ghedira, 2021. "A hybrid evolutionary approach to job-shop scheduling with generic time lags," Journal of Scheduling, Springer, vol. 24(3), pages 329-346, June.
    20. T. C. E. Cheng & Bo Peng & Zhipeng Lü, 2016. "A hybrid evolutionary algorithm to solve the job shop scheduling problem," Annals of Operations Research, Springer, vol. 242(2), pages 223-237, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1350-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.